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7 March 2022 Machine learning for ultrafast photonics applications: from nonlinear instabilities to broadband supercontinuum generation
Goëry Genty
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Proceedings Volume PC11999, Ultrafast Phenomena and Nanophotonics XXVI; PC119990E (2022) https://doi.org/10.1117/12.2611092
Event: SPIE OPTO, 2022, San Francisco, California, United States
Abstract
We review the use of machine learning techniques in ultrafast photonics applications with emphasis on fiber-optics systems. In particular, we discuss how neural networks can be used to extract quantitative time-domain information in the development of nonlinear instabilities from spectral intensity measurements. We also show how neural networks can be efficiently applied to predict nonlinear dynamics in optical fibres for a wide range of scenarios, from pulse compression to ultra-broadband supercontinuum generation in both single and multimode fibers.
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Goëry Genty "Machine learning for ultrafast photonics applications: from nonlinear instabilities to broadband supercontinuum generation", Proc. SPIE PC11999, Ultrafast Phenomena and Nanophotonics XXVI, PC119990E (7 March 2022); https://doi.org/10.1117/12.2611092
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KEYWORDS
Supercontinuum generation
Machine learning
Ultrafast phenomena
Photonics
Nonlinear optics
Neural networks
Numerical integration
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